RESUMO
This study aimed to investigate which of the two frequently adopted perfusion models better describes the contrast enhanced ultrasound (CEUS) perfusion signal in order to produce meaningful imaging markers with the goal of developing a machine-learning model that can classify perfusion curves as benign or malignant in breast cancer data. Twenty-five patients with high suspicion of breast cancer were analyzed with exponentially modified Gaussian (EMG) and gamma variate functions (GVF). The adjusted R2 metric was the criterion for assessing model performance. Various classifiers were trained on the quantified perfusion curves in order to classify the curves as benign or malignant on a voxel basis. Sensitivity, specificity, geometric mean, and AUROC were the validation metrics. The best quantification model was EMG with an adjusted R2 of 0.60 ± 0.26 compared to 0.56 ± 0.25 for GVF. Logistic regression was the classifier with the highest performance (sensitivity, specificity, Gmean, and AUROC = 89.2 ± 10.7, 70.0 ± 18.5, 77.1 ± 8.6, and 91.0 ± 6.6, respectively). This classification method obtained similar results that are consistent with the current literature. Breast cancer patients can benefit from early detection and characterization prior to biopsy.
RESUMO
We present a case of sonographically detected transient gas in the portal vein in a 4.5-month-old infant who had a history of two consecutive jejunectomies due to jejunal stenoses and was admitted to our hospital with clinical and laboratory findings consistent with a subacute small bowel obstruction and dehydration. Sonography excluded other pathologies and the patient was treated conservatively with success. The presence of gas in the portal vein could be a sign of an underlying mechanical obstacle, as another episode of small bowel obstruction 1 month later required surgical treatment of adhesive intestinal obstruction.